Contributed by Matt Breslin | President, IFS Americas
Walk the floor at DTECH, and one thing becomes clear quickly: utility leaders are no longer debating whether artificial intelligence (AI) belongs in their organizations. That question is settled. The more urgent debate is far more pragmatic: where AI is already working, what it’s worth, and how fast it can pay back in the parts of the business that keep the lights on.
This shift is happening against a backdrop of intensifying operational risk. Extreme weather is placing sustained pressure on outage management and restoration. Workforce shortages are shrinking the pool of experienced field talent. Aging assets are being asked to perform beyond their original design life, even as load growth becomes larger, more concentrated, and more volatile. The U.S. Department of Energy has warned that grid reliability risks could rise sharply without sufficient additions of firm capacity, underscoring how decisively the reliability conversation has returned to the forefront.
There is a second accelerant at play: AI is both a solution and a driver of demand. Goldman Sachs Research forecasts that global data‑center power demand will increase by 50% by 2027 and by as much as 165% by 2030 compared to 2023 levels, posing planning and cost challenges. At the same time, utilities are being asked to modernize operations.
Reliability and affordability are back in the driver’s seat
Sustainability remains central to utility strategy, but conversations at DTECH reflected a reset toward the fundamentals: reliability and affordability. After years of pilots and experimentation, many organizations are experiencing “AI fatigue.” Not from lack of belief in technology, but from difficulty deploying it at scale and realizing value quickly.
This scrutiny is reinforced by the sheer capital required to modernize the grid. BloombergNEF estimates annual global grid investment must reach $811 billion by 2030 under its Net Zero Scenario, nearly triple recent investment levels. That reality is forcing utilities to demand measurable returns from digital investments, not just compelling demonstrations.
Why pilots stall: the execution gap
Over the past several years, utilities have explored AI across outage prediction, asset inspection, maintenance optimization, and scheduling. Some pilots delivered insight, but many failed to scale. The pattern is familiar: models generated recommendations, but execution in the field didn’t change.
At DTECH, multiple sessions emphasized the same friction points: data quality, integration with operational systems, and governance across IT and OT. In outage management discussions, including a joint session with our customer Exelon, the conversation focused far less on algorithms and far more on process readiness, platform integration, and change management.
These details rarely appear in pilot summaries, but they ultimately determine whether AI survives contact with operations.
Where AI is delivering measurable impact today
The strongest AI success stories at DTECH d one thing in common: outcomes tied directly to operational KPIs.
1) Outage management and storm response
Utilities are using AI‑driven analytics to improve outage prediction, prioritize restoration work, and optimize crew deployment during major weather events. These tools augment human judgment when conditions change rapidly, improving situational awareness and helping leaders make faster, more consistent decisions under pressure.
2) Predictive and condition‑based maintenance
Predictive maintenance remains one of the most value‑rich AI use cases because it directly reduces unplanned outages and extends asset life. Industry data shows that condition‑based and predictive approaches can significantly reduce maintenance costs and improve reliability—especially when AI insights are connected to governed data and automated workflows rather than isolated dashboards
3) Field workforce planning, scheduling, and dispatch
This is where speed‑to‑value becomes tangible. Utility field service benchmarks from IFS show organizations using AI-powered planning and scheduling optimization reporting improvements, such as:
- 35% increase in technician productivity
- 16% improvement in SLA compliance
- 40% reduction in travel time
- 16.6% increase in jobs completed per day
- 8.75% reduction in travel distance
These metrics matter because they translate AI into operational language: fewer truck rolls, less windshield time, higher first‑time completion, and more predictable service levels.
4) Digital workers to augment the workforce
Another pragmatic shift emerging from DTECH is how often leaders talked about AI as a way to remove repetitive work, not replace skilled workers. Digital workers can take on high‑volume, rules‑based tasks that consume time and attention, especially in the workflows that support field execution.
Increasingly, these capabilities are being delivered through governed, extensible AI platforms—such as IFS Loops—that allow organizations to deploy digital workers within existing operational systems and adapt them as needs evolve. The value for utilities is straightforward: reduce administrative friction so field and operations teams can focus on reliability, safety, and customer restoration, while maintaining control, traceability, and trust.
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Speed‑to‑value, with governance, becomes the gate
If one phrase dominated serious AI conversations at DTECH, it was speed‑to‑value. Utilities are under pressure to show benefits earlier, even as timelines compress. At the same time, leaders emphasized the need for trusted, governed data foundations spanning planning, operations, fieldwork, and customer engagement, because scaling AI depends on consistent definitions, security controls, and traceability across IT and OT.
In practice, utilities are applying four filters to AI investments post‑DTECH:
- Is the data fit‑for‑purpose and governed?
- Does the AI change a workflow, not just a dashboard?
- Can it be deployed in months, not years?
- Can success be measured in operational KPIs?
Bottom line: the era of AI theater is ending
The takeaway from DTECH isn’t that AI is new to utilities—it’s that expectations have matured. AI will not be judged by model sophistication. It will be judged by whether it improves reliability, safety, and efficiency in the field, fast enough to matter amid rising load growth and mounting operational risk.
The era of pilots is giving way to production‑grade execution. For an industry built on accountability, that shift is not only inevitable- it’s overdue.
About the Author
Matt Breslin leads the North American IFS team, driving growth and ensuring the company’s success across key industries. Matt’s role focuses on industrial AI and cloud adoption across new and existing customers, working with them to power their business transformation and deliver exceptional Moments of Service to their clients.
Over the past 25 years, Matt has worked with some of the biggest names in the software industry and has held senior positions at Upland Software, Infor, SAP, and Oracle. He holds an MBA from Northwestern University’s Kellogg School of Management and an undergraduate degree from the University of Notre Dame.
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